Systems and methods for detecting abnormalities within a circuit of an electrosurgical generator

ABSTRACT

An electrosurgical generator includes primary and test sources. The primary source supplies a primary signal and the test source supplies a test signal. The electrosurgical generator includes an output circuit and an abnormality detection circuit. The output circuit is electrically coupled to the primary and test sources. The output circuit receives the primary and test signals from the primary and test sources, respectively. The output circuit is electrically coupled to a load to supply the primary signal thereto. The abnormality detection circuit is electrically coupled to the output circuit to detect an abnormality therein as a function of the test signal. The abnormality detection circuit can also determine a location of the abnormality within the output circuit.

CROSS REFERENCE TO RELATED APPLICATION

The present application claims the benefit of and priority to U.S.Provisional Application Ser. No. 61/776,523, filed on Mar. 11, 2013, theentire contents of which are incorporated herein by reference.

BACKGROUND

1. Technical Field

The present disclosure relates to electrosurgery. More particularly, thepresent disclosure relates to systems and methods for detecting anabnormality within a circuit of an electrosurgical generator.

2. Description of Related Art

Electrosurgery involves the application of high-frequency electriccurrent to treat, cut or modify biological tissue during a surgicalprocedure. Electrosurgery is performed using an electrosurgicalgenerator, an active electrode, and a return electrode. Theelectrosurgical generator (also referred to as a power supply orwaveform generator) generates an alternating current (AC), which isapplied to tissue through the active electrode and is returned to theelectrosurgical generator through the return electrode. The alternatingcurrent usually has a frequency above 100 kilohertz to avoid muscleand/or nerve stimulation.

During electrosurgery, the alternating current generated by theelectrosurgical generator is conducted through tissue disposed betweenthe active and return electrodes. The tissue's impedance converts theelectrical energy (also referred to as electrosurgical energy)associated with the alternating current into heat, which causes thetissue temperature to rise. The electrosurgical generator controls theheating of the tissue by controlling the electric power (i.e.,electrical energy per time) provided to the tissue. Although many othervariables affect the total heating of the tissue, increased currentdensity correlates to increased heating. Electrosurgical energy istypically used for cutting, dissecting, ablating, coagulating, and/orsealing tissue.

The two basic types of electrosurgery are monopolar and bipolarelectrosurgery. Both types of electrosurgery use an “active” and a“return” electrode. In bipolar electrosurgery, the surgical instrumentincludes an active electrode and a return electrode on the sameinstrument or in very close proximity, usually causing current to flowthrough a smaller amount of tissue. In monopolar electrosurgery, thereturn electrode is located elsewhere on the patient's body and istypically not part of the electrosurgical instrument itself. Inmonopolar electrosurgery, the return electrode is part of a deviceusually referred to as a return pad.

Electrosurgical generators may perform various self-tests.Electrosurgical generators test internal and external components todetermine if one or more abnormalities are present. Some of theself-tests that electrosurgical generators perform occur during startupand are typically referred to as power-on self-tests. Self-tests mayalso occur during operation of the electrosurgical generator, includingduring a surgical procedure. These tests facilitate safe, efficientand/or accurate operation of the electrosurgical generator.

SUMMARY

In one aspect, the present disclosure features a method of abnormalitydetection includes: generating primary and tests signals within anelectrosurgical generator; applying the primary and test signals to acircuit of the electrosurgical generator; receiving the primary and testsignal from the circuit; detecting an abnormality within the circuit asa function of the received test signal; and determining a location ofthe abnormality within the circuit. The method may also include:modulating the test signal in accordance with a maximum length sequencealgorithm; and cross-correlating the receiving signal with the testsignal. The method may also include: generating an impulse signaldefining the test signal; determining an impulse response of the circuitas a function of the received test signal; and/or detecting anabnormality within the circuit as a function of the impulse response.

The method may include: generating a multi-sine signal; determining thelinear frequency response function of the circuit from the received testsignal at the fundamental frequencies of the multi-sine signal; and/ordetermining the non-linear frequency response of the circuit from thereceived test signal at the even and/or odd frequency components of themulti-sine signal; and/or detecting an abnormality within the circuit asa function of the linear and/or non-linear responses.

The abnormality may be a short within the output circuit, an opencircuit within the output circuit, an abnormality of a resistor withinthe output circuit, an abnormality of a sensor coupled within the outputcircuit, an abnormality of a coil within the output circuit, a circuitcomponent of the output circuit being different than a predeterminedvalue, the circuit component of the output circuit being different thana calibrated value, and/or the circuit component of the output circuitbeing outside of a predetermined range of values.

The test signal may be modulated using a multisine algorithm, apseudo-random noise algorithm, a chirp algorithm, and/or a swept sineimpetus algorithm. The test signal may be generated such that it issubstantially or statistically orthogonal to the primary signal, e.g.,the test signal may be a pseudo-random noise signal defining the testsignal such that the test signal is statistically uncorrelated to theprimary signal, thereby improving the signal-to-noise ratio (SNR) of theselected test method.

The test signal may be applied during a power-on self test of theelectrosurgical generator. The test signal may be narrowband limited ororthogonal.

In another aspect, the present disclosure features a method forabnormality detection in an electrosurgical generator, which includes:generating an impulse signal defining a test signal; generating amaximum length sequence (MLS) having a period greater than the length ofthe impulse response of the circuit to be measured in theelectrosurgical generator; converting the MLS into a bi-phasic MLS ofnormalized or unit amplitude values; modulating the test signal inaccordance with the converted MLS; applying successive bursts of thetest signal to the input of the circuit; receiving the test signal fromthe output of the circuit; demodulating the received test signal toobtain a received MLS; cross-correlating the converted MLS with thereceived MLS to obtain the impulse response of the circuit; anddetecting an abnormality within the circuit based on the impulseresponse of the circuit.

Cross-correlating the bi-phasic MLS with the received MLS may include:inserting a zero value into the first element of the received MLS;permuting the received MLS according to a first permutation matrix;adding a zero value in the front of the first permutation matrix toobtain a first permuted MLS; applying a transform to the first permutedMLS; deleting the first element of the transformed MLS; permuting thetransformed MLS according to a second permutation matrix to obtain asecond permuted MLS; and dividing the second permuted MLS by the lengthof the MLS. The transform may be a Fast Walsh-Hadamard Transform and thereceived MLS may be the average of successive received MLSs. The MLS maybe constructed according to the equation:

${a_{n} = {\sum\limits_{i = l}^{r}\; {c_{i}a_{n - i}}}},$

where a_(n) is the nth value of the MLS and c_(i) is ith coefficient ofthe primitive polynomial of degree r>1.

BRIEF DESCRIPTION OF THE DRAWINGS

Various embodiments of the present disclosure are described herein withreference to the drawings wherein:

FIG. 1A shows a graphical illustration of an electrosurgical system inaccordance with embodiments of the present disclosure;

FIG. 1B shows a block diagram of an electrosurgical generator of theelectrosurgical system of FIG. 1 in accordance with embodiments of thepresent disclosure;

FIG. 2A shows a block diagram of a generator circuit including an outputcircuit and an abnormality detection circuit based on a modifiedKahn-technique, high efficiency, amplitude modulated electrosurgicalgenerator in accordance with an embodiment of the present disclosure;

FIG. 2B shows a block diagram of a generator circuit and an abnormalitydetection circuit based on a Class S, high-efficiency, pulse-widthmodulated electrosurgical generator in accordance with a furtherembodiment of the present disclosure;

FIG. 3 shows a block diagram of a generator circuit including an outputcircuit and an abnormality detection circuit in accordance with a stillfurther embodiment of the present disclosure;

FIGS. 4A-6B show current and voltage sensors used for abnormalitydetection in an electrosurgical generator in accordance with embodimentsof the present disclosure;

FIGS. 7A-7F show system-level block diagrams representing a maximumlength sequence algorithm for modulating and receiving the test signalutilized by the abnormality detection circuit of FIG. 3 in accordancewith an embodiment of the present disclosure;

FIG. 8 shows a flow diagram of a method for abnormality detection inaccordance with embodiments of the present disclosure; and

FIGS. 9 and 10 show flow diagrams of a method for abnormality detectionusing a maximum length sequence (MLS) technique in accordance withfurther, embodiments of the present disclosure.

DETAILED DESCRIPTION

Particular embodiments of the present disclosure are describedhereinbelow with reference to the accompanying drawings. In thefollowing description, well-known functions or constructions are notdescribed in detail to avoid obscuring the present disclosure inunnecessary detail.

FIG. 1A shows a graphic illustration of a bipolar and monopolarelectrosurgical system 100 in accordance with an embodiment of thepresent disclosure. The electrosurgical system 100 includes anelectrosurgical generator 102 capable of detecting an abnormality andthe location of the abnormality therewithin (described below). Thegenerator 102 performs monopolar and bipolar electrosurgical procedures,including vessel sealing procedures. The generator 102 may include aplurality of outputs (e.g., terminals 104 and 106) for interfacing withvarious electrosurgical instruments (e.g., a monopolar active electrode108, a return pad 110, bipolar electrosurgical forceps 112, a footswitch(not shown), etc. Further, the generator 102 includes electroniccircuitry that generates radio frequency power specifically suited forvarious electrosurgical modes (e.g., cutting, blending, division, etc.)and procedures (e.g., monopolar treatment, bipolar treatment, vesselsealing, etc.).

The system 100 includes a monopolar electrosurgical instrument 114having one or more electrodes 108 for treating tissue of a patient(e.g., electrosurgical cutting probe, ablation electrode(s), etc.).Electrosurgical RF current is supplied to the instrument 114 by thegenerator 102 via a supply line 116, which is connected to an activeterminal 104 of the generator 102, allowing the instrument 114 tocoagulate, ablate and/or otherwise treat tissue. The RF current isreturned from electrode 108 through tissue to the generator 102 via areturn line 118 of the return pad 110 at a return terminal 106 of thegenerator 102. The active terminal 104 and the return terminal 106 mayinclude connectors (not explicitly shown) configured to interface withplugs (also not explicitly shown) of the instrument 114 and the returnelectrode 110, which are disposed at the ends of the supply line 116 andthe return line 118, respectively.

The system 100 also includes return electrodes 120 and 122 within returnpad 110 that are arranged to minimize the chances of tissue damage bymaximizing the overall contact area with the patient's tissue. Inaddition, the generator 102 and the return electrode 110 may beconfigured for monitoring so-called “tissue-to-patient” contact toinsure that sufficient contact exists therebetween to further minimizechances of tissue damage.

The system 100 also includes a bipolar electrosurgical forceps 112having one or more electrodes (e.g., electrodes 124 and 126) fortreating tissue of a patient. The instrument 112 includes opposing jawmembers 134 and 136 having an active electrode 124 and a returnelectrode 126 disposed therein, respectively. The active electrode 124and the return electrode 126 are connectable to the generator 102through cable 128, which includes a supply line 130 and a return line132 coupled to the active terminal 104 and the return terminal 106,respectively. The instrument 112 is coupled to the generator 102 at aconnector having connections to the active terminal 104 and returnterminal 106 (e.g., pins) via a plug (not explicitly shown) disposed atthe end of the cable 128, wherein the plug includes contacts from thesupply line 130 and the return line 132.

The generator 102 may be any suitable type (e.g., electrosurgical,microwave, etc.) and may include a plurality of connectors toaccommodate various types of electrosurgical instruments (e.g.,instrument 114, electrosurgical forceps 112, etc.). Further, thegenerator 102 may be configured to operate in a variety of modes such asablation, monopolar and bipolar cutting, coagulation, and other modes.It is envisioned that the generator 102 may include a switchingmechanism (e.g., relays) to switch the supply of RF energy between theconnectors, such that, for instance, when the instrument 114 isconnected to the generator 102, only the monopolar plug receives RFenergy. The active terminal 104 and return terminals 106 may be coupledto a plurality of connectors (e.g., inputs and outputs) of the generator102 to power a variety of instruments.

The generator 102 includes suitable input controls (e.g., buttons,activators, switches, touch screen, and the like) for controlling thegenerator 102. In addition, the generator 102 may include one or moredisplay screens for providing the user with a variety of outputinformation (e.g., intensity settings, treatment complete indicators,etc.). The controls allow the user to adjust power of the RF energy,waveform, and other parameters to achieve the desired waveform suitablefor a particular task (e.g., coagulating, tissue sealing, intensitysetting, etc.). The instruments 112 and 114 may also include a pluralityof input controls that may be redundant with certain input controls ofthe generator 102. Placing the input controls at the instruments 112 and114 allow for easier and faster modification of RF energy parametersduring the surgical procedure without requiring interaction with thegenerator 102.

FIG. 1B shows a block diagram of the electrosurgical generator 102 ofFIG. 1A including a generator circuit 105 in accordance with anembodiment of the present disclosure. The generator circuit 105 includesa controller 150 and an output stage 151 which is controlled by thecontroller 150. The output stage 151 includes a high voltage powersupply (HVPS) 152 and a radio frequency (RF) output stage 154. Thecontroller 150 includes a microprocessor 156 and a memory 157. Themicroprocessor may be any suitable microcontroller, microprocessor(e.g., Harvard or Von Neuman architectures), PLD, PLA, CPLD, FPGA, orother suitable digital logic. Memory 157 may be volatile, non-volatile,solid state, magnetic, or other suitable storage memory.

Controller 150 may also include various circuitry (e.g., amplifiers,buffers and the like) to provide an interface between microprocessor 156and other circuitry of the generator circuit 105. Controller 150receives various feedback signals that are analyzed by microprocessor156 to provide control signals in response thereto. The controls signalsfrom controller 150 control the HVPS 152 and the RF output stage 154 toprovide electrosurgical energy to tissue, which is represented by a loadresistor R_(L) 160.

The HVPS 152 includes a power circuit 158. The power circuit 158supplies a suitable electric current to the RF output stage 154. The RFoutput stage 154 converts the current from the power circuit 158 toelectrosurgical energy for application to the load resistor R_(L) 160.For example, the HVPS 152 provides a DC signal to the RF output stage154 that generates the electrosurgical energy using push-pull orH-bridge transistors coupled to a primary side of a step-up transformerwith a resonant load matching network (not explicitly shown).

FIG. 2A illustrates generator circuitry 200 of an electrosurgicalgenerator (e.g., a high-efficiency, amplitude-modulated, resonant RFelectrosurgical generator) according to some embodiments of the presentdisclosure. The generator circuitry 200 includes an output circuit 201coupled to a controller circuit 203, which includes an abnormalitydetector 234 for detecting abnormalities in the output circuit 201. Theabnormalities may be detected using a modified Kahn technique asdescribed in more detail below. The output circuit 201 includes voltagesource 205, converter 208, inverter 214, and resonant filter 220. Theoutput of the voltage source 205 is electrically connected to the inputof the converter 208, the output of the converter 208 is electricallyconnected to the input of the inverter 214, the output of the inverter214 is electrically connected to the input of the resonant filter 220,and the output of the resonant filter 220 is configured to deliverenergy to tissue, the impedance of which is represented by the loadresistor 226. The output circuit 201 also includes a plurality ofvoltage sensors 204, 210, 216, and 222, and a plurality of currentsensors 206, 212, 218, and 224, each of which are electrically connectedto the output of one of the voltage source 205, the converter 208, theinverter 214, and the resonant filter 220.

The voltage source 205 provides direct current to the converter 208,which increases the voltage of the direct current. The converter 208provides the converted direct current to the inverter 214, which invertsconverted direct current to an alternating current. The inverter 214receives synchronization signals from an oscillator 232 of thecontroller circuit 203. In this way, the inverter 214 can generate analternating current having an appropriate frequency for electrosurgery.The resonant filter 220 enables the transfer of substantially maximumpower to load resistor 226 by resonating characteristics of the outputcircuit 201 to characteristics of the load resistor 226. Additionally,the sensed results from the voltage sensor 222 and the current sensor224 have higher importance than the other sensed results because theoutput of the resonant filter 220 is directly connected to the patient.For this reason, the sensed results of the voltage sensor 222 and thecurrent sensor 224 are also provided to the compensator sampler 238.

The number and placement of voltage and current sensors may varydepending upon the circuitry used in the output circuits 201 and 251 togenerate electrosurgical energy. Also, voltage and current sensors maybe placed within the different subcircuits of the output circuits 201and 251 to obtain different and more granular measurements. For example,one or more voltage and current sensors may be placed at appropriatepoints within the inverter 252 or resonant filter 220.

The controller circuit 203 includes the multiplexer 228, abnormalitysampler 230, abnormality detector 234, compensator sampler 238,compensator 240, generator reference setter 242, abnormality referencesetter 244, abnormality indicator 248, two oscillators 232 and 236, andan adder 246. The multiplexer 228 receives sensed results from all thevoltage and current sensors, selects one or more sensed results, andsends the selected results to abnormality sampler 230. The compensatorsampler 238 receives the sensed results of the output of the resonantfilter 220. Both the abnormality sampler 230 and the compensator sampler238 are synchronized with the frequency of the alternating currentgenerated by the inverter 214 to filter the received sensed results fromthe voltage and current sensors by the carrier oscillator 232. Thecarrier oscillator 232 may be a voltage-controlled oscillator or anumerically-controlled oscillator.

The compensator 240 receives the filtered samples from the compensatorsampler 238 and compensates fluctuations of the filtered samples over atime period. One example of compensating circuits is aproportional-integral-derivative (“PID”) controller. The result of thecompensator 240 is then provided to the carrier oscillator 232 and thegenerator reference setter 242.

The carrier oscillator 232 takes the output of the compensator 240 intoconsideration and provides appropriate synchronization signals to theinverter 214, the abnormality sampler 230, and the compensator sampler238.

The generator reference setter 242 receives the compensated results fromthe compensator 240 and sets an appropriate reference power profile thatcan be used as a reference in detecting abnormalities in the outputcircuit 201. The reference power profile is then provided to theabnormality reference setter 244. With the reference power profile, theabnormality reference setter 244 sets tolerance ranges for voltage andcurrent of each of circuits in the output circuit 201. The abnormalityreference is then provided to the abnormality detector 234 and theabnormality detector 234 checks whether sampled results of themultiplexer 228 are within a tolerance range specified in theabnormality reference. If the result is in the tolerance range, theabnormality detector 234 outputs no abnormality and, if the results arenot within the tolerance range, outputs abnormality.

For example, if the multiplexer 228 selects results from the output ofthe inverter 214, the abnormality reference setter 244 sets toleranceranges of the output of the inverter 214 based on the reference powerprofile provided by the generator reference setter 242. The selectedresults by the multiplexer 228 are sampled by the abnormality sampler230. The abnormality detector 234 then compares the sampled output ofthe abnormality sampler 230 with the tolerance ranges of the abnormalityreference setter 244. If the sampled output is out of the tolerancerange, the abnormality detector 234 then finds abnormality in theinverter 214.

The test oscillator 236 receives the result of the abnormality detector234 and generates a test signal having a frequency is different from thefrequency generated by the carrier oscillator 232. The test oscillator236 may generate a signal of which frequency is specific to a circuitwhere an abnormality is found. For this embodiment, the test oscillator236 may generate four different signals with four different frequencieswhich are different from the frequency generated by the carrieroscillator 232. In order to have meaningful results from each sensor andfrom the abnormality sampler 230 and the compensator sampler 238, thefour different frequencies are less than the frequency generated by thecarrier oscillator 232.

The signal generated by the test oscillator 236 and the result of thecompensator are added by the adder 246 and the added signal is thenprovided to the converter 208 so that the test signal for detectingabnormality is propagated into the output circuit 201.

The abnormality detector 234 may also provide the abnormality result tothe abnormality indicator 248 to indicate which circuit has abnormalityto an operator of the electrosurgical generator and the operator cantake appropriate actions to correct the abnormality and to preventpossible harm to a patient.

FIG. 2B illustrates generator circuitry 250 for a Class S,high-efficiency, pulse width modulated resonant electrosurgicalgenerator according to other embodiments of the present disclosure. Thegenerator circuitry 250 includes an output circuit 251 and a controlcircuit 253. Instead of converter 208 and inverter 214 of FIG. 2A, theoutput circuit 251 of FIG. 2B includes inverter 252. Also, the controlcircuit 253 includes a digital pulse width modulation (DPWM) unit 258for generating and providing a DPWM control signal to the inverter 252.

A method of detecting an abnormality in a system includes applying atest signal to the system, measuring the frequency response functionsbetween any two sets of sensors in the system, and comparing themeasured frequency response functions (FRFs) with the expected variationlimits of the FRFs for a normal system between any two sets of sensorsin the system. The abnormality of the system under test may be definedas occurring when at least one of several possible conditions isdetected:

-   -   1. The FRF magnitude, which is typically defined as |H(s)| for        gain and |Z(s)| for impedance (which are described in more        detail below), at the test frequency deviates by more than a        predetermined maximum value.    -   2. The FRF phase, which is typically defined as arg(H(s)) for        gain and arg(H(s)) for impedance (which are described in more        detail below), at the test frequency deviates by more than a        predetermined maximum value.    -   3. There is more distortion and noise energy (defined below)        present in the output spectrum after going through the network        between sensors, e.g., sensors 204, 206, 216, and 218, than a        predetermined maximum value.

By testing the FRF against any combination of these conditions, theabnormality detection system can detect not only components or groups ofcomponents that have open- and short-circuited in the signal pathbetween the sets of sensors, but also components or groups of componentsthat have partially failed or that output the wrong value. Theabnormality detection system may also detect intermittent abnormalitiesas long as they are manifest over a sufficient portion of themeasurement period. The last condition (condition 3.) may be helpful inrevealing non-linear behavior resulting from an abnormality that ismanifest as distortion outside of the fundamental frequency of interest.

In addition to abnormality detection, one may perform (simultaneously)calibration of one, or more, circuits within a system between sets ofsensors using either or both internal or externally attached loads. Onemay connect a known load resistance, or impedance, and measure the FRF,then, by the ratio of the FRF to the expected nominal FRF, applyfrequency-dependent magnitude and phase corrections.

The FRFs may be transfer functions (i.e., gains) or impedances. Thefollowing transfer functions may be useful for abnormality detection andisolation:

-   -   1. Voltage gain defined as

${{H_{V}(s)} = \frac{V_{B}(s)}{V_{A}(s)}},$

where V_(B)(s) is the Laplacian domain voltage at output sensor B andV_(A)(s) is the Laplacian domain voltage at input sensor A.

-   -   2. Current gain defined as

${{H_{I}(s)} = \frac{I_{B}(s)}{I_{A}(s)}},$

where I_(B)(s) is the Laplacian domain current at output sensor B andI_(A) (s) is the Laplacian domain current at input sensor A.

-   -   3. Input impedance defined as

${Z_{i}(s)} = {\frac{V_{A}(s)}{I_{A}(s)}.}$

-   -   4. Load impedance defined as

${Z_{I}(s)} = {\frac{V_{B}(s)}{I_{B}(s)}.}$

-   -   5. Output impedance defined as

${Z_{o}(s)} = {{Z_{1}(s)}{\frac{{V_{B}(s)} - {V_{A}(s)}}{V_{A}(s)}.}}$

The location of an abnormality can be narrowed down to the groups ofcomponents that are disposed between the sets of sensors of these FRFsusing the gain transfer functions and further isolated using impedanceand distortion information. More sets of sensors may be added to furtherisolate even smaller groups of components as required by risk assessmentand desired product features. The testing may be performed as part of aself-test, e.g., off-line, at any time and it may also be performedcontinuously during operation of the system, e.g., on-line, as long asthe test signal is either designed to be of a nominal energy level ascompared to the energy contained in the primary signal, i.e., thetherapeutic signal. Alternatively, the test signal may be designed to beincluded as part of the primary signal energy, or may even be thecontrol signal itself. Testing against a subset of these criteria mayyield a useful set of possible abnormalities, which depends upon theposition of the sets of sensors used for the test within the system andthe use cases and requirements of the operational environment inquestion.

A first step for determining an abnormality is to ensure a priori, i.e.,at the time of design of the system, that the signal to noise ratio(SNR) of the measurement is sufficient for determining an abnormality,i.e., the measured response to the test signal is significantly lower invariance for a normal system under test than the just-detectablevariance of the abnormalities.

The swept single-sine method (including a chirp) has been used to obtainhigh-fidelity FRFs and distortion analysis. However, the length of timerequired to obtain good SNR for low frequency signals and theintrusiveness of the method in performing on-line measurement of anactive system have opened the door to development and use of otheralternative methods over the past couple of decades. The sweptsingle-sine method is best applied off-line during calibrationprocedures or during power-on self-tests (POSTs).

A single-impulse method does not generally yield a very good SNR for FRFmeasurements and may be less helpful in distortion analysis. Often,multiple impulse tests are performed and averaged over time to improvethe SNR, which tends to lengthen test times and make the single-impulsemethod less desirable over swept single-sine methods. Therefore, it maybe best to apply the swept single-sine method off-line duringcalibration procedures or POST, especially for purposes of distortionanalysis. Also, an averaging of simple random-noise tests may beperformed over long periods of time to obtain satisfactory SNR to makean FRF measurement.

The Maximum Length Sequence (MLS) test, where the noise is a priorichosen as a pseudo-random sequence to allow for correlation of thereceived test signal with the sourced signal, is generally considered abetter test in terms of obtaining satisfactory results over relativelyshort test times with minimal invasiveness and little or no additionalaveraging time necessary. The MLS test may be applied online during RFactivations or off-line during calibration procedures or POST.

With respect to SNR, the measured energy, £, for the single-sine testsignal can be written, using Parseval's Theorem for the discrete Fouriertransform (DFT) relation, as the sum of three components: DC, AC, andnoise. This may be expressed algebraically as:

$\begin{matrix}{ɛ = {{\sum\limits_{n = 0}^{N - 1}\; {x_{n}}^{2}} = {{\frac{1}{N}{{\hat{X}}_{0}}^{2}} + {\frac{1}{N}{{\hat{X}}_{1}}^{2}} + {\frac{1}{N}{\sum\limits_{k \neq 1}\; {{\hat{X}}_{k}}^{2}}}}}} & (1)\end{matrix}$

where x_(n) is the discrete-time series of DFT window length N for themeasured periodic signal including exactly one complete cycle of the ACcomponent (i.e., coherently sampled), {circumflex over (λ)}₀ is the DCcomponent, {circumflex over (λ)}₁ is the complex AC component of thetest signal (i.e., the excited or fundamental component), and{circumflex over (λ)}_(k) are the complex distortion and noisecomponents in the unexcited harmonics of the AC fundamental component.It is also possible to uniquely identify harmonics, or select harmonics,of this distortion as well. These components may be extracted from themeasured discrete-time series as follows:

$\begin{matrix}{{\hat{X}}_{0} = {\sum\limits_{n = 0}^{N - 1}\; {x_{n}\mspace{14mu} {and}}}} & (2) \\{{\hat{X}}_{1} = {\sum\limits_{n = 0}^{N - 1}\; {{x_{n}\left\lbrack {{\cos \left( {\frac{2\pi}{N} \cdot n} \right)} - { \cdot {\sin \left( {\frac{2\pi}{N} \cdot n} \right)}}} \right\rbrack}.}}} & (3)\end{matrix}$

This is a complex single-frequency DFT.

The noise energy may be derived from (1)-(3) by subtracting the AC andDC components from the total signal power:

$\begin{matrix}{{{\hat{ɛ}}_{noise} = {{\sum\limits_{n = 0}^{N - 1}\; {x_{n}}^{2}} - \left\lbrack {{\frac{1}{N}{{\hat{X}}_{0}}^{2}} + {\frac{1}{N}{{\hat{X}}_{1}}^{2}}} \right\rbrack}},} & (4)\end{matrix}$

while the resulting SNR is the ratio of the AC signal power to the noiseenergy of expression (4):

$\begin{matrix}{{SNR} = {\frac{\frac{1}{N}{{\hat{X}}_{1}}^{2}}{{\hat{ɛ}}_{noise}}.}} & (5)\end{matrix}$

This SNR must be greater than the abnormality threshold to be measured,which is some fraction c₁ of the expected normal AC test component:

$\begin{matrix}{{SNR} > {\left\lbrack {\frac{c_{1}}{N}{{\hat{X}}_{1}}^{2}} \right\rbrack^{- 1}.}} & (6)\end{matrix}$

For the multisine FRF measurement one may extend expression (1) tomultiple excitation frequencies, which may be randomized in respectivephases:

$\begin{matrix}{{ɛ = {{\sum\limits_{n = 0}^{N - 1}\; {x_{n}}^{2}} = {{\frac{1}{N}{{\hat{X}}_{0}}^{2}} + {\frac{1}{N}{\sum\limits_{m}\; {{\hat{X}}_{m}}^{2}}} + {\frac{1}{N}{\sum\limits_{k \neq m}\; {{\hat{X}}_{k}}^{2}}}}}},} & (7)\end{matrix}$

where {circumflex over (λ)}_(m) are a series of m multisine ACcomponents of the test signal, and {circumflex over (λ)}_(k) are thedistortion and noise components in the unexcited harmonics (i.e.excluding the fundamental components m) of the multisine AC components.These individual components, also assuming coherent sampling, maysimilarly be extracted from the measured discrete-time series accordingto the following equation:

$\begin{matrix}{{\hat{X}}_{m} = {\sum\limits_{n = 0}^{N - 1}\; {{x_{n}\left\lbrack {{\cos \left( {\frac{2\pi}{N}{m \cdot n}} \right)} - { \cdot {\sin \left( {\frac{2\pi}{N}{m \cdot n}} \right)}}} \right\rbrack}.}}} & (8)\end{matrix}$

This is also a complex single-frequency DFT at frequency

$f_{m} = {\frac{2\pi}{N}{m.}}$

The noise energy may be selected values sεk of unexcited DFT bins givenby:

$\begin{matrix}{{\hat{ɛ}}_{noise}^{\prime} = {\frac{1}{N}{\sum\limits_{s \in {k \neq m}}{{{\hat{X}}_{s}}^{2}.}}}} & (9)\end{matrix}$

These selected bins are determined a priori. One approach is to simplyuse all of the unexcited bins. Another approach is to drop one or morebins due to a need for reduced computation time or non-idealities in themeasurement technique resulting from short lengths of N and frequencysmearing, or bleeding, between DFT frequency bins from intermodulationcomponents. An advantage of looking at selected bins or combinations ofbins in the multisine technique is that distortion products due tofailed or failing components will create stronger than normal harmoniccontent relative to the AC fundamental component that may be observed inthese bins. For example, saturation due to voltage overdrive will resultin a measurable relative increase in the odd harmonics.

The resulting SNR for multisine at any particular excitation frequencyeεm may be expressed as:

$\begin{matrix}{{S\; N\; R^{\prime}} = {\frac{\frac{1}{N}{\sum\limits_{e \in m}{{\hat{X}}_{e}}^{2}}}{{\hat{ɛ}}_{noise}^{\prime}}.}} & (10)\end{matrix}$

This SNR must be greater than the abnormality threshold to be measured,which is some fraction c_(e) of the expected normal component:

$\begin{matrix}{{SNR} > \left\lbrack {\frac{1}{N}{\sum\limits_{e \in m}{c_{e}{{\hat{X}}_{e}}^{2}}}} \right\rbrack^{- 1}} & (11)\end{matrix}$

Conversely, the selected unexcited components could be used to detectabnormalities, when they are greater than the expected value. While thisis true of both single-sine tests as well as multisine, multisine allowsfor a more rapid determination of this situation with a sufficientlylong DFT (or, more practically, Fast Fourier Transform (FFT)).

The SNR may be improved by averaging multiple measurements over time,assuming that the noise is random. This is because the averaging processresults in a coherent addition of the sinusoids of interest and anon-coherent addition of the noise. Such an improvement is referred toas processing gain. But processing gain may also be achieved by anyindividual or combination of methods employing pre-emphasis andde-emphasis of the originating stimulus test signal spectrum, e.g.,increasing the amplitudes of the higher frequency components of the testsignal to compensate for a low-pass frequency response of the system orcircuit tinder test by applying an inverse function of the normalresponse. This is referred to as leveling or equalization. Averaging isessential for random-noise tests, especially when combined withleveling, and it can significantly improve MLS tests to the point ofbeing nearly indistinguishable in fidelity to swept single sine tests.

There are a number of ways to do averaging. One way is vector averagingof the received abnormality detector DFT spectra. Each averaged pairincreases the SNR by 3 dB. The advantage of vector averaging is that itmaintains phase information. In vector averaging, the complex values,e.g., the real and imaginary components of equation (3), are averaged asopposed to averaging of the overall magnitudes or root mean square(r.m.s) averaging. Vector averaging requires coherent, and optionallysynchronous, sampling, i.e., the abnormality detector data samplerwindow must be triggered and data samples taken at a rate that isrelated by integer multiples of the AC test components and their phases.Since the controller circuits 203 and 253 generate the test signal andthe control signal while digitally sampling the sensors, synchronous andcoherent sampling can be guaranteed.

Careful consideration may be given a priori to the Crest Factor of thetest signal employed. The Crest Factor is given by the peak, g_(∞)(u),to root mean square (r.m.s), g₂(u), ratio for a discrete-time series,u(n). The Crest Factor in this case is computed according to theequation:

$\begin{matrix}{{{CF}(u)} = {\frac{g_{\infty}(u)}{g_{2}(u)} = \frac{\max\limits_{n \in {\lbrack{0,{N - 1}}\rbrack}}{{u(n)}}}{\sqrt{\frac{1}{N}{\sum\limits_{n = 0}^{N - 1}{{u(n)}}}}}}} & (12)\end{matrix}$

Test signals in the form of an impulse signal, a multisine signal, and arandom noise signal (e.g., a maximum length sequence (MLS) signal) allhave high Crest Factors relative to the single-sine test signal. HighCrest Factors reduce the signal-to-noise ratio (SNR) and overall qualityof the measurement. The objective of these test signals, such as in thecase of more versatile multisine tests, is to minimize the Crest Factorto optimize the SNR.

Generally, swept single-sine test signals have the best SNR with thelowest Crest Factor with respect to all other types of test signals.Leveling and averaging can be applied to the other types of test signalsto reduce the Crest Factor and to optimize the SNR. Tests using theimpulse test signal, however, must be repeated periodically and theinverse of the leveling function must be applied to the test signal.Applying leveling and averaging to random noise and MLS test signals mayimprove SNR comparable to the SNR of the swept single-sine test signals,but the Crest Factors may be an order of magnitude higher than the CrestFactor of a swept single-sine test signal that is leveled and averaged.

In other embodiments, the test signal may be a multisine excitation testsignal, which straddles the solution sets of MLS signals and sweptsingle-sine signals. The multisine excitation test sequence includes asum of sinusoids, which are not necessarily harmonically related, eachwith its own phase with respect to the start of the sequence. Themultisine excitation test sequence may be given by the equation:

$\begin{matrix}{{{u\lbrack n\rbrack} = {\sum\limits_{m = 1}^{M}{a_{m}{\cos \left( {{2{\pi \cdot f_{m} \cdot n}} + \phi_{m}} \right)}}}},} & (13)\end{matrix}$

where M is the number of sinusoids, φ_(m) is the phase of each sinusoidwith respect to the start of the sequence, a_(m) are the excitationfundamental amplitudes, and f_(m) are the excitation frequencies. Thephase φ_(m) may be randomized between [−π,π) to reduce the Crest Factorand thereby improve the SNR.

FIG. 3 shows a block diagram of a generator circuit 300 in accordancewith a still further embodiment of the present disclosure. The generatorcircuit 300 includes an output circuit 304, an abnormality sampler 306,a microprocessor 394, which includes an abnormality detector 396, amicrocontroller 340, and a pulse width modulator (PWM) 350. Themicrocontroller 340 includes a primary signal generator that generates aprimary signal I_(primary) 362, which is provided to an input of the PWM350. The microprocessor 394, which may be implemented by themicrocontroller 340, includes a test signal generator 390, a switchtester 392, an abnormality detector 396, and an abnormality locationdetector 398. The test signal generator 390 generates a test signalI_(test) 360 that is provided to another input of the PWM 350.

The PWM 350 modulates the primary signal 362 with the test signal 360and generates PWM signals based on the modulated primary signal 362 tooperate the switches 342, 344, 346, and 348 of the H-bridge inverter341. The output circuit 304 is electrically coupled to the load resistor226. The microprocessor 394 and the abnormality sampler 306 areelectrically coupled to the outputs of the HVPS 202 and the outputsensors 356 of the output circuit 304.

The microcontroller 340 provides a primary signal 362 for application tothe input node 312 of the circuit being tested 365, which includes anH-bridge inverter 341, a resonant matching network 352, and an outputtransformer 354, via the PWM 350. In embodiments of the presentdisclosure, the circuit being tested 365 is any circuit which supplieselectrical energy to a load and may include (or may be) a supply line,one or more conductors, a cable, a multiple path circuit and/or anysuitable circuitry to supply electrical energy from an input node (e.g.,input node 312) to an output node (e.g., output nodes 314 and 316).

The microprocessor 394 supplies the test signal I_(test) 360 to the PWM350, which modulates the primary signal 362 with the test signal 360,generates a PWM signal based on the modulated primary signal 362, andprovides the PWM signal to the circuit being tested 365 via input node312. The abnormality detector 396 can detect one or more abnormalitieswithin the circuit being tested 365 or the output circuit 304 via theabnormality sampler 306. The abnormality sampler 306 receives andsamples the sensed input and output currents and voltages from outputcircuit 304. The microprocessor 394 includes the abnormality detector396 which processes these sensed current and voltage signals to detectan abnormality within the output circuit 304.

The output circuit 304 includes a current sensor 315 and a voltagesensor 325 coupled to the input of the circuit being tested 365. Thecurrent sensor 315 includes a resistor 334 that is coupled in seriesbetween the HVPS 202 and the circuit being tested 365. The voltagesensor 325 includes resistors 336 and 338 coupled together in series ina voltage divider configuration. The abnormality sampler 306 samples thevoltages at nodes 318 and 320 to measure the current through theresistor 334. Additionally, the microcontroller 340 receives the outputcurrent and voltage sensed by the output sensors 356 at the output nodes314 and 316. The microcontroller 340 utilizes the sensed output currentand voltage to control the generation of the primary signal 362.

Referring to FIGS. 4A-6B, several alternative current and voltagesensors are shown that are usable by the output circuits 201, 251, and304 of FIGS. 2A, 2B, and 3.

FIG. 4A shows a circuit diagram of an embodiment of a current sensor forsensing current flowing, for example, between nodes 318 and 320 of theoutput circuit 304. The current sensor includes an iron currenttransformer 400 having a first coil coupled between the nodes 318 and320 and having a second coil coupled in parallel with resistor 334(R_(sense)). The current I_(sense) 328, which represents the currentflowing between the nodes 318 and 320, is obtained by measuring thevoltage across the resistor 334.

FIG. 4B shows a circuit diagram of another embodiment of a currentsensor 450 that includes an air core Rogowski coil to sense current. Thecurrent sensor 450 includes an integrator 455 and a Rogowski coil, whichis represented by a resistance R_(T) 460, a capacitance C_(T) 465, andan inductance L 475. Hs indicates the sensitivity of the Rogowski coiland H_(S)·I is a voltage 470 induced by current I flowing through aninductor 480 coupled to an output circuit. A terminal voltage across thecapacitance C_(T) 465 causes current to flow through the resistanceR_(T) 460 and the integrator 455 sums the current flowing through theresistance R_(T) 460 and provides a voltage. The current flow throughthe Rogowski coil is then determined by measuring the voltage across theoutputs of the integrator 455.

FIG. 5A shows a voltage sensor that includes a single-ended voltagetransformer 500 having an iron core for coupling to the circuit beingtested 365 (FIG. 3) via, for example, a ground and node 368 to generatethe sensed voltage signal V 330. FIG. 5B shows another embodiment of avoltage sensor including a capacitive, single-ended voltage transformer550. The capacitive single-ended voltage transformer 550 includes twocapacitors: an input-side capacitor 560 and a terminal-side capacitor570. The input voltage is stepped down by the two capacitors 560 and 570and a terminal voltage is output across the terminal-side capacitor 570.

FIG. 6A shows a voltage sensor including an isolated, differential, ironcore voltage transformer 600 for coupling, for example, to the output ofthe circuit being tested 365 (FIG. 3) via RF active node 314 and RFreturn node 316 to provide output signal V 332 representative of thedifference between voltages of the RF active node 314 and the RF returnnode 316. FIG. 6B is another embodiment of a voltage sensor including adifferential, capacitive voltage transformer 650 that includes twoinput-side capacitors 660 and 670, a terminal-side capacitor 680, and aterminal-side resistor 690. This voltage sensor measures a difference involtage between the input terminals and steps it down to a desiredoutput voltage value.

Referring again to FIG. 3, the abnormality detector 396 can detect anabnormality within the circuit being tested 365 utilizing the testsignal 360. The abnormality detection may occur during a power-onself-test (i.e., during a POST routine), and the abnormality detector396 may be calibrated to the circuit being tested 304. In someembodiments, capacitors 370 and 372 couple the abnormality sampler 306to the current sensor 315, capacitor 374 couples the abnormality sampler306 to the voltage sensor 325, and capacitors 375 and 376 couples theabnormality sampler 306 to the output sensors 356, which includes anoutput current sensor and an output voltage sensor (not shown). Thecapacitors 375 and 376 may filter out the primary signal 362 and/or maybe DC blocking capacitors.

The abnormality sampler 306 includes notch filters 381, 383, 385, and387, and bandpass filters 382, 384, 386, and 388. Each of the notchfilters 381, 383, 385, and 387 are coupled to a respective bandpassfilters 382, 384, 386, and 388. The microprocessor 394 receives anoutput voltage signal from the output sensors 356 via notch filter 381and bandpass filter 382. The microprocessor 394 receives an outputcurrent signal from the output sensors 356 via notch filter 383 andbandpass filter 384. The microprocessor 394 receives an input voltagesignal from node 322 of the voltage sensor 325, which is a voltagedivider including resistors 336 and 338 via notch filter 385 andbandpass filter 386. The microprocessor 394 receives an input currentsignal from the current sensor 315 via notch filter 387 and bandpassfilter 388. Additionally, microprocessor 394 may detect the voltage atnode 318 via the notch filter 387 and the bandpass filter 388.

The microprocessor 394 may be a digital signal processor (not explicitlyshown), and/or may be implemented in software, hardware, firmware,virtualization, PLAs, PLD, CPLD, FPGA and the like. Additionally oralternatively, the microcontroller 340 and the microprocessor 394 may beintegrated together, e.g., such as within a digital signal processor,and may include a watchdog timer. The microprocessor 394 utilizes thetest signal generator 390 thereby facilitating the operation of theabnormality detector 396 and the abnormality location detector 398 indetecting and determining the location of an abnormality within theoutput circuit 304. Additionally, the test signal generator 390operatively instructs the PWM 350 to selectively control switches 342,344, 346, and 348 to determine an abnormality within the switches 342,344, 346, and 348.

The test signal 360 is applied to input node 312 thereby affecting theinput current and voltage signals sensed at node 368 and the outputvoltage and current signals sensed at output nodes 314 and 316 by theoutput sensors 356. The test signal generator 390 controls thegeneration of the test signal I_(test) 360 thereby affecting the inputand output current and voltage signals to detect an abnormality withinthe output circuit 304, and to determine the location of the abnormalitytherewithin. The microprocessor 394 and the microcontroller 340 utilizea single set of non-redundant sensors. However, in other embodiments,the sensors may be redundant. The abnormality may be a short within theoutput circuit 304, an open circuit within the output circuit 304, anabnormality of a resistor (e.g., one or more of resistors 334, 336, 338)within the output circuit 304, an abnormality of a sensor coupled withinthe circuit being tested 304, an abnormality of a coil (e.g., of anoutput transformer (not shown) coupled between output nodes 314 and 316to provide a step-up voltage) within the output circuit 304, a circuitcomponent (e.g., the resistors 334, 336, and/or 338) of the outputcircuit 304 being different than a predetermined value, the circuitcomponent (e.g., the resistors 334, 336, and/or 338) of the outputcircuit 304 being different than a calibrated value, the circuitcomponent (e.g., the resistors 334, 336, and/or 338) of the outputcircuit being outside of a predetermined range of values, and/or thelike.

The bandpass filters 382, 384, 386, and 388 are tunable to obtainfrequency information. The frequency information includes the frequencyof the test signal 360. The frequency information may be received via adigital or analog signal. The bandpass filters 382, 384, 386, and 388are tuned to the test signal 360. The notch filters 381, 383, 385, and387 have a center frequency that filters out the primary signal 362. Asmentioned previously, the tunable bandpass filters 382, 384, 386, and388, and the notch filters 381, 383, 385, and 387 may be implemented insoftware or by utilizing a digital signal processor.

Microprocessor 394 may detect an abnormality and its location bydetermining the system ID of the circuit being tested 365, using ohm'slaw calculation, and/or circuit analysis to detect discrepancies orfailures of the resistors or sensors (e.g., resistors 336, 338, and334). For example, the microprocessor 394 can control the PWM 350 togenerate an impulse signal defining the test signal 360. Themicroprocessor 394 receives the impulse signal from the output sensors356 to detect an abnormality and determine the location of theabnormality as a function of the impulse response of the output circuit.Microprocessor 394 may also detect an abnormality and its location byutilizing other algorithms including swept-sine, chirp, and/orpseudo-random noise impetus signals. Additionally or alternatively,microprocessor 394 may detect an abnormality and its location byutilizing various algorithms to determine the system ID of the circuitbeing tested 304, including algorithms utilizing swept-sine, chirp,and/or pseudo-random noise impetus signals.

Microprocessor 394 is in operative communication with microcontroller340 (in some embodiments, the microcontroller 340 and the microprocessor394 are integrated together). In one embodiment of the presentdisclosure, microprocessor 394 detects abnormalities while themicrocontroller 340 is disabled; and the microprocessor 394 determinesthe accuracy of one of resistors 334, 336, and 338 or switches 342, 344,346, and 348, and communicates to the microcontroller 340 adjustmentvalues for adjusting the primary signal 362. Additionally,microprocessor 394 may test output circuit 304 with or without the loadresistor 226.

Abnormality detector 396 may instruct PWM 350 to output A, B, C, and Dsignals to control the switches 342, 344, 346, and 348. Moreparticularly, the test signal generator 390 can operatively disablemicrocontroller 340 (or at least disable output of the primary signal362 from the microcontroller 340) and instruct PWM 350 to apply a testsignal to selectively switch switches 342, 344, 346, and 348. Themicroprocessor 394 can utilize the sensed input and output voltages andcurrents to determine whether one or more of switches 342, 344, 346, and348 has an abnormality. In some embodiments, other switches (not shown)may disconnect the load resistor 226. In other embodiments, groups ofswitches 342, 344, 346, and 348 are activated by microprocessor 394 sothat microprocessor 394 can determine if one or more of the switches342, 344, 346, and 348 are operating properly. In yet other embodiments,switches 342, 344, 346, and 348 are tested during a power-on self test.

As mentioned above, the test signal I_(test) 360 may be narrowbandlimited or orthogonal to the primary signal I_(primary) 362. Forexample, the test signal I_(test) 360 may utilize a pseudo-random noisesequence that is orthogonal (uncorrelated) to the primary signalI_(primary) 362. Additionally or alternatively, abnormality sampler 306may be phase locked with the microprocessor 394, e.g., using aphase-locked loop to track a frequency-hopping microprocessor 394.

The test signal I_(test) 360 may incorporate a minimum or maximum lengthsequence (MLS) and may be used to extract the impulse response of thecircuit being tested 365. See CMDA: Principles of Spread SpectrumCommunication, Addison-Wesley, 1995. The following equation can be usedto generate an MLS of period, P=2^(r)−1:

$\begin{matrix}{{a_{n} = {\sum\limits_{i = 1}^{r}{c_{j}a_{n - i}}}},} & (14)\end{matrix}$

where a_(n) is the next desired sequence value and c_(i) are thecoefficients of the primitive polynomial of degree r>1. The values forc_(i) may be from tables for primitive polynomials of various degrees insources such as Error Correcting Codes, by E. J. Weldon and W. W.Peterson, MIT Press, Cambridge, Mass., 1972.

To find the impulse response of an unknown system, h[n], such as theoutput circuit 304, the test signal generator 390 may apply the MLSalgorithm to the test signal I_(test) 360. By using a[n], the outputresponse is given by the convolution of h[n] and a[n]:

y[n]=h[n]*a[n].  (15)

By utilizing circular cross-correlation, the following equation isobtained:

φ _(sy) =h[n]* φ _(ss).  (16)

But, because, by definition, the autocorrelation φ _(ss) is an idealimpulse function, i.e.:

φ _(ss)≠δ_(r) [n],  (17)

it follows that:

h[n]= φ _(sy).  (18)

The method for determining the system impulse results includes: (1)drive the test signal I_(test) 360 using a repeating sequencea_(1-[n])[n]; (2) measure the response y[n]; and (3) perform a circularcross-correlation of y[n] with a_(r)[n] to produce ĥ[n−Δ], which is theΔ-delayed estimate of h[n].

In some embodiments, a least mean squares (LMS) filter may be employedto generate a model of a circuit of the electrosurgical generator thatis being tested in order to determine whether there is an abnormality inthe circuit. The circuit may be described as an unknown system h(n) tobe modeled or identified and the LMS filter adapts the filter ĥ(n),which represents an estimate of the model of the circuit, to make it asclose as possible to h(n). An abnormality may be detected in aparticular circuit by comparing the adapted filter ĥ(n), whichrepresents the current model of the particular circuit, to apredetermined filter ĥ(n)′, which represents the same type of circuitthat is operating normally. If there is a difference between the adaptedfilter ĥ(n) and the predetermined filter ĥ(n)′, characteristics of thatdifference may be used to determined the type of abnormality.

FIG. 7A is a detailed block diagram of an LMS filter according to anembodiment of the present disclosure. The LMS filter, which may be afinite impulse response (FIR) filter, includes a series of time delayunits 702 a-702 n and a series of weighting units 704 a-704 n coupled toa digital input test signal x_(k). During operation, the first weightingunit 704 a multiplies the digital input signal x_(k) by the first weightvalue w_(0k) of the weight vector w _(k+1). The time delay units 702b-702 n shift the digital input test signal x_(k) and correspondingweighting units 704 b-704 n multiply the delayed digital input testsignal x_(k) by corresponding weight values w_(1k), . . . , w_(Lk) ofthe weight vector w _(k+1). The results of time delaying and weightingthe digital input test signal x_(k) are added together by an adder 706to obtain the output signal y_(k).

The output signal y_(k) is fed back to a LMS weight adaptation unit, inwhich the output signal y_(k) is subtracted from the desired responsesignal d_(k), which would be the output from the actual circuit beingmodeled, by a subtractor 708 to obtain an error signal e_(k). The errorsignal e_(k) and the input test signal are then used in the followingLMS update equation to compute the weight vector updates:

_(k+1) = w _(k)+2μe _(k) x _(k),  (19)

where μ is chosen by the designer and is bounded:

${0 < \mu < \frac{1}{\lambda_{\max}}},$

where λ_(max)≦trace( Λ)=trace( R). Or, more simply:

$\begin{matrix}{{0 < \mu < \frac{1}{\left( {L + 1} \right)\left( {{Signal}\mspace{14mu} {Power}\mspace{14mu} {of}\mspace{14mu} {\overset{\_}{x}}_{k}} \right)}},} & (20)\end{matrix}$

where L is the filter length.

FIGS. 7B-7F show the structure for implementing the time delay units 702b-702 n with a fractional fixed delay of l/m samples. FIGS. 7B-7E showthe multi-rate structure for realizing a fixed delay of l/m samples. Asshown in FIG. 7B, the multi-rate structure is an all-pass filter havingunity gain (FIG. 7C) and a fractional delay (which is given by the slopeshown in the graph of FIG. 7D). FIG. 7E shows the details of themulti-rate structure. As shown, an input test signal x(n) is applied toan interpolator 710, which up-samples the input test signal x(n) by afactor of M to obtain an up-sampled or interpolated signal v(m). Theup-sampled signal v(m) is then filtered by a digital lowpass filter 712to remove the images (i.e., the extra copies of the basic spectrum)created by the interpolator 710. The resulting filtered signal u(m) isthen delayed by l samples by a delay unit 714 and down-sampled by afactor of M in the decimator 716 to obtain an equalized output signaly(n).

FIG. 7F is a diagram of an efficient polyphase implementation of themulti-rate structure of FIG. 7A. This implementation includes a seriesof transversal FIR filters 718 a-718 k that filter the input test signalx(n). The transversal FIR filters 718 a-718 k are given by the followingdifference equation:

p _(r)(n)=h _(LP)(nM+r),  (21)

where 0≦r≦(M−1). The delay of l is implemented as a new initial positionof the commutator switch (“P selector”) 720 corresponding to the sampleat n=0.

FIG. 8 shows a flow chart diagram of a method 800 for abnormalitydetection in accordance with the present disclosure. The method 800includes steps 801-818. After starting in step 801, a primary signal isgenerated within an electrosurgical generator in step 802. In step 804,a test signal is generated within the electrosurgical generator, e.g.,using an MLS algorithm or impulse signal. Next, in step 806, the primarysignal and the test signal are applied to an output circuit of theelectrosurgical generator. In step 808, the primary signal and the testsignal (e.g., the MLS modulated signal or impulse signal) are receivedfrom the output circuits. In step 810, the primary signal isautocorrelated with the test signal. In step 812, the impulse responseof the output circuit is determined as a function of the received testsignal. In step 814, an abnormality is detected with the output circuitas a function of the received test signal (e.g., using an impulseresponse). Then, before ending in step 818, the location of theabnormality within the output circuit is determined in step 816.

As described above, the test oscillator 236 may be modulated using amaximum length sequence (MLS) and may be used to extract the FRF of thecircuit at any sensor distal to the test oscillator 236. A method forperforming an MLS test is illustrated in FIG. 9.

After starting in step 901, an impulse signal defining a test signal isgenerated in step 905. In step 910, an MLS with a period greater thanthe impulse response of the desired circuit to be measured is generated(or obtained from a look-up table) based on the a priori known length ofthe circuit's impulse response in the time domain using, for example,the following equation:

n[k]=n(k)⊕n(k+2),  (22)

where the operator ⊕ denotes an exclusive-or (XOR) (modulo-2 sum)operation, and k is the sequence index for the “M-sequence” n[k] oflength K=2^(N)−1, consisting of N stages, initialized to 1s.

The M-sequence may then be used to create a K×K matrix consisting ofrows, each of which is successively left circularly shifted (or delayed)of the original sequence in the first row. For example, a seven symbolM-sequence given by 1, 1, 1, 0, 0, 1, 0 may generate a matrix M givenby:

$M = {\begin{bmatrix}1 & 1 & 1 & 0 & 0 & 1 & 0 \\1 & 1 & 0 & 0 & 1 & 0 & 1 \\1 & 0 & 0 & 1 & 0 & 1 & 1 \\0 & 0 & 1 & 0 & 1 & 1 & 1 \\0 & 1 & 0 & 1 & 1 & 1 & 0 \\1 & 0 & 1 & 1 & 1 & 0 & 0 \\0 & 1 & 1 & 1 & 0 & 0 & 1\end{bmatrix} = {A\; {B.}}}$

This matrix may then be decomposed into K×N and N×K matrices that may bereferred to as “tag” matrices A and B, respectively. B is the first Nrows of matrix M, i.e.:

$B = {\begin{bmatrix}1 & 1 & 1 & 0 & 0 & 1 & 0 \\1 & 1 & 0 & 0 & 1 & 0 & 1 \\1 & 0 & 0 & 1 & 0 & 1 & 1\end{bmatrix}.}$

A may be obtained by evaluating the following equation:

A=B ^(T)σ⁻¹,  (23)

where B^(T) is a transposed matrix of B and σ⁻¹ is the matrix inverse ofσ, which is an N×N matrix of B, or the first N columns of B, i.e.:

$\sigma = {\begin{bmatrix}1 & 1 & 1 \\1 & 1 & 0 \\1 & 0 & 0\end{bmatrix}.}$

Taking the matrix inverse of σ results in the following matrix:

$\sigma^{- 1} = {\begin{bmatrix}0 & 0 & 1 \\0 & 1 & 1 \\1 & 1 & 0\end{bmatrix}.}$

Thus, equation (23) may be evaluated using the matrices B^(T) and σ⁻¹ toobtain matrix A:

$A = {\begin{bmatrix}1 & 0 & 0 \\0 & 1 & 0 \\0 & 0 & 1 \\1 & 1 & 0 \\0 & 1 & 1 \\1 & 1 & 1 \\1 & 0 & 1\end{bmatrix}.}$

In step 915, the generated MLS of 0s and 1s are converted to a bi-phasicsequence of normalized or unit amplitude values, e.g., 0 is converted to1 and 1 is converted to −1. In step 920, the test signal is modulated inaccordance with the bi-phasic MLS sequence. Then, in step 925, at leasttwo successive bursts of the test signal modulated with the MLS areapplied to the input of the desired circuit of the electrosurgicalgenerator while receiving, in step 930, the test signal at the outputfrom the desired circuit using a sensor or sensor pair coupled to theoutput. An initial burst may be used to allow transient settling, whilethe second or more successive bursts may be used for the measurements.The average of successive bursts may be calculated to improve the SNR.

In step 935, the received test signal is demodulated to obtain areceived MLS. Then, in step 940, the received MLS is cross-correlatedwith the converted MLS to obtain the impulse response of the desiredcircuit. Before ending in step 955, an abnormality within the desiredcircuit is detected in step 945 based on the impulse response of thedesired circuit.

In embodiments, the received MLS may be cross-correlated with theconverted MLS to obtain the impulse response of the desired circuit byusing a suitable transformation algorithm. FIG. 10 illustrates such analgorithm. After starting in step 1001, an MLS (or an average MLS) isreceived and a zero value is inserted into the first element of thereceived MLS in step 1005. Then, in step 1010, the MLS is permuted(i.e., re-ordered) according to a first permutation matrix Ps to obtaina first permuted MLS. This is done to simplify the computation of thetransform, such as the Fast Walsh-Hadamard Transform, and is analogousto the operations of “padding zeros” and permutation for simplifyingFFTs.

In step 1015, the transform, such as the Fast Walsh-Hadamard Transformis applied to the first permuted MLS matrix. This is a cross-correlationfunction that selects the time-aligned impulse response data, whilerejecting non-time-aligned or uncorrelated noise.

In step 1020, the first element of the transformed MLS is deleted andthe result is permuted, or re-ordered, in step 1025, according to asecond permutation matrix P_(L) to obtain a second permuted MLS which isa row matrix as the tag matrix B. Before ending in step 1035, the secondpermuted MLS is divided by the length of the MLS, i.e., K+1, in step1030 to obtain an estimated time-domain impulse response. This isanalogous to the reordering done in FFTs. In embodiments, the estimatedtime-domain impulse response may be changed to the frequency domain byperforming an FFT.

While several embodiments of the disclosure have been shown in thedrawings, it is not intended that the disclosure be limited thereto, asit is intended that the disclosure be as broad in scope as the art willallow and that the specification be read likewise. Therefore, the abovedescription should not be construed as limiting, but merely asexemplifications of particular embodiments. Those skilled in the artwill envision other modifications within the scope and spirit of theclaims appended hereto.

What is claimed is:
 1. A method for abnormality detection in anelectrosurgical generator, the method comprising: generating primary andtests signals within an electrosurgical generator; applying the primaryand test signals to a circuit of the electrosurgical generator;receiving the primary and test signals from the circuit; detecting anabnormality within the circuit as a function of the received testsignal; and determining a location of the abnormality within thecircuit.
 2. The method according to claim 1, further comprising:generating an impulse signal defining the test signal; determining animpulse response of the circuit as a function of the received testsignal; and detecting an abnormality within the circuit as a function ofthe impulse response.
 3. The method according to claim 1, furthercomprising: modulating the test signal in accordance with a maximumlength sequence algorithm; and cross-correlating the received testsignal with the generated test signal.
 4. The method according to claim1, wherein the abnormality is one of a short within an output circuit,an open circuit within the output circuit, an abnormality of a resistorwithin the output circuit, an abnormality of a sensor coupled within theoutput circuit, an abnormality of a coil within the output circuit, acircuit component of the output circuit being different than apredetermined value, the circuit component of the output circuit beingdifferent than a calibrated value, and the circuit component of theoutput circuit being outside of a predetermined range of values.
 5. Themethod according to claim 1, wherein the test signal is applied during apower-on self test of the electrosurgical generator.
 6. The methodaccording to claim 1, further comprising modulating the test signalaccording to at least one of a pseudo-random noise algorithm, a chirpalgorithm, and a swept sine impetus algorithm.
 7. The method accordingto claim 1, further comprising generating a pseudo-random noise signaldefining the test signal such that the test signal is orthogonal to theprimary signal.
 8. The method according to claim 1, wherein the testsignal is selected from the group consisting of narrowband limited andorthogonal.
 9. A method for abnormality detection in a circuit of anelectrosurgical generator, the method comprising: generating an impulsesignal defining a test signal; generating a maximum length sequence(MLS) having a period greater than the length of an impulse response ofthe circuit to be measured in the electrosurgical generator; convertingthe MLS into a bi-phasic MLS of normalized or unit amplitude values;modulating the test signal in accordance with the converted MLS;applying successive bursts of the test signal to the input of thecircuit; receiving the test signal from the output of the circuit;demodulating the received test signal to obtain a received MLS;cross-correlating the converted MLS with the received MLS to obtain theimpulse response of the circuit; and detecting an abnormality within thecircuit based on the impulse response of the circuit.
 10. The methodaccording to claim 9, wherein cross-correlating the bi-phasic MLS withthe received MLS comprises: inserting a zero value into the firstelement of the received MLS; permuting the received MLS according to afirst permutation matrix; adding a zero value in the front of the firstpermutation matrix to obtain a first permuted MLS; applying a transformto the first permuted MLS; deleting the first element of the transformedMLS; permuting the transformed MLS according to a second permutationmatrix to obtain a second permuted MLS; and dividing the second permutedMLS by the length of the MLS.
 11. The method according to claim 10,wherein the transform is a Fast Walsh-Hadamard Transform.
 12. The methodaccording to claim 9, wherein the received MLS is the average ofsuccessive received MLSs.
 13. The method according to claim 9, whereinthe MLS is constructed according to the equation:${a_{n} = {\sum\limits_{i = 1}^{r}{c_{i}a_{n - i}}}},$ where a_(n) isthe nth value of the MLS and c_(i) is the ith coefficient of theprimitive polynomial of degree r>1.